Towards Privacy-Preserving Driver’s Drowsiness and Distraction Detection: A Differential Privacy Approach

Abstract

The ubiquitous need for detecting driver’s drowsiness and distraction by using a camera mounted inside the car directed to driver’s face is driving significant concern about protecting driver’s identity from being discovered or exploited by hackers. In this paper, we present a novel technique called block Laplacian Obfuscation Mechanism (bLOM), to privatize the camera data stream by using differential privacy techniques introduced in database domain. We introduce a metric to measure privacy and utility for driver’s drowsiness and distraction algorithms. Our experimental results show that in 87% of test cases, bLOM is able to keep the identity of the driver private while still be able to extract facial features needed for driver’s drowsiness and distraction detection.